Recommending media programs based on media program popularity

ABSTRACT

A computer-implemented method includes receiving information expressing a user&#39;s interest in one or more media programs, obtaining information indicative of popularity for a plurality of media programs responsive to the received information by individuals other than the user, and transmitting one or more recommendations of media programs for display to the user, from the plurality of media programs that relate to the received information.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation application of, and claims priorityto, pending U.S. patent application Ser. No. 11/844,883, filed on Aug.24, 2007, entitled “Recommending Media Programs Based on Media ProgramPopularity”. The disclosure of the foregoing application is incorporatedherein by reference in its entirety.

TECHNICAL FIELD

This document discusses systems and methods for providing viewers andlisteners of media programs with media-based recommendations, such asfor television shows and movies.

BACKGROUND

Internet searching has become perhaps the most-performed activity on theWorld Wide Web. Various search engines may crawl web-accessibledocuments, index them, and deliver links to them in response to relatedrequests received from users. Search results may be based, for example,on commonality of terms between a search request and words or phrases ina particular document (or synonyms of the words or phrases), and/or uponlinks between various pages, as with the Google® PageRank™ system.Internet users may conduct searches, for example, on media-relatedproperties, such as television programs, music, and movies.Alternatively, users of the Web may receive recommendations, such asreviews of books, consumer products, and other items, from various otherusers.

SUMMARY

This document describes systems and methods that may be employed toassist users in finding media programming in which they may have aninterest, and that can provide additional information about, or accessto, that programming. Generally, the systems and methods permit, incertain implementations, for a user to obtain recommendations aboutmedia programs. Generally, such recommendations differ from data likesearch results, in that search results are a response to an explicitrequest, whereas recommendations are one or more steps removed from thespecific request, and are based more on inferences gleaned from specificrequests and other information.

For example, a user's search requests may be compared to other searchrequests to find users with common interests to the first user, andmedia-based preferences of those other users (gleaned, for example, fromtheir media-based search requests or from programs they have queued inpersonal programming directories) may be used to form recommendationsfor the first user. In one particular example, recommendations may bemade using popularity information gleaned from web search and clickdata. In addition, audience measurement data (e.g., television ratingsdata) may be used, and data to be used for recommendations maytransition from audience measurement data to web search and click dataas additional web search and click data becomes available over time.Other techniques, approaches, and systems for making media-basedrecommendations are also described here.

In one implementation, a computer-implemented method is disclosed. Themethod comprises receiving information expressing a user's interest inone or more media programs, obtaining information indicative ofpopularity for a plurality of media programs responsive to the receivedinformation by individuals other than the user, and transmitting one ormore recommendations of media programs for display to the user, from theplurality of media programs that relate to the received information. Thereceived information may comprise an explicit query, and may includeinformation indicative of media programs viewed by the user. The methodmay also comprise generating data for displaying an electronic programguide grid that includes the one or more recommendations. Also, theinformation indicative of popularity can comprise search activity orclick activity related to the search activity, or information indicativeof popularity may comprise audience measurement data. In otherinstances, the information indicative of popularity can comprise searchactivity or click activity related to the search activity, and the oneor more recommendations can be selected based on a weighting determinedby a quality measure of the search activity or click activity.

In certain aspects, the method further comprises obtaining informationindicative of audience participation and web search activity relating tomedia programs, and making a recommendation substantially using theaudience participation data if the web search activity does not permitmaking a recommendation of sufficiently high quality. Also, the methodcan include analyzing past activity by the user to determine topics ofinterest to the user, and making recommendations of programs associatedwith the topics of interest. Moreover, the method can also includedetermining co-dependency between the user's interest and the pluralityof media programs using collaborative filtering.

In another implementation, a computer-implemented system is disclosed.The system comprises memory storing data relating to popularity of oneor more media programs, an interface configured to receive media-relatedrequests, to generate responses to the requests, and to generaterecommendations different from the responses, wherein therecommendations include information about the one or more media programsand are based at least in part on the popularity of the one or moremedia programs, and a programming guide builder to generate code forconstructing a programming guide containing programs responsive to themedia related requests and displaying popularity indications for theprograms in the guide. The recommendations can be generated from acombination of web activity data and audience measurement data. Also,the interface can be configured to transition from applying audiencemeasurement data to applying web search activity data as a qualitymeasure of the web search activity data improves.

In some aspects, the system can include a collaborative filter fordetermining co-dependency relationships between users and mediaprograms. Also, the data relating to popularity can comprise webactivity data and audience measurement data associated with the mediaprograms. The programming guide can comprise a listing of upcomingepisodes of programs identified by the recommendations, and the systemmay further comprise a collaborative filter for determiningco-dependency among programs of the one or more media programs, for usein generating the recommendations.

In yet another implementation, a computer-implemented system comprisesmemory storing data relating to popularity of one or more mediaprograms, means for generating recommendations from the media programsin response to a user submission of a media-related identifier, and aprogramming guide builder to generate code for constructing aprogramming guide containing programs relating to the recommendations.The data relating to popularity can comprise web activity data andaudience measurement data associated with the media programs. Also, theprogramming guide can comprise a listing of upcoming episodes ofprograms identified by the recommendations.

The details of one or more embodiments are set forth in the accompanyingdrawings and the description below. Other features, objects, andadvantages will be apparent from the description and drawings, and fromthe claims.

DESCRIPTION OF DRAWINGS

FIG. 1A is an example screen shot showing recommendations associatedwith a television episode.

FIG. 1B is an example screen shot showing recommendations associatedwith multiple media programs in a search result list.

FIG. 1C is an example screen shot showing recommendations associatedwith a television program.

FIG. 2 shows a graph of a general correlation between two forms ofaudience preference data over time.

FIG. 3 is a schematic diagram of a computer-implemented system forproviding media-related recommendations over a network.

FIG. 4A is a flow chart of a process for providing media-relatedrecommendations in response to a search query.

FIG. 4B is a flow chart of a process for providing media-relatedrecommendations.

FIG. 4C is a flow chart of a process for providing media-relatedrecommendations for new programs

FIG. 5 is a swim lane diagram showing interaction of various componentsin a media-related recommendation process.

FIG. 6 shows an example of a computer device and a mobile computerdevice that can be used to implement the techniques described here.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

FIGS. 1A-1C show various screen shots from a system for generatingmedia-related recommendations, such as recommendations for movies,music, television programs, and the like. Media-related recommendationsare suggestions provided to users regarding programs, movies, songs,etc., that they are likely to enjoy. Recommendations are different fromsearch results, in that the latter are a direct response to a specificquery, while the former may be much more general. Specifically,recommendations are related to, but are not provided as part of, asearch result list.

Generally, recommendations depend on some sort of identifiedco-dependency between two objects. For example, the objects can be twousers or two TV programs. Where the objects are two users, a firstuser's interests can be tracked, other users with similar interests canbe identified, and then additional topics that may interest those otherusers can be recommended to the first user. Where the objects are two TVprograms, the actual content of the programs can be analyzed to findsimilarities. For example, two programs may share certain actors, orthey may share a particular genre. Multiple such parameters may beshared between two programs, making their correlation even greater.

Recommendations may be generated with or without explicit user input.For explicit input, a user may enter a search query, and recommendationsmay be generated in response to the entry of the search query. Fornon-explicit, or implicit input, the recommendations may look to datarelated to a user that is not from a formal submission of a query by theuser. Recommendations may be generated, for example, while a user isreviewing an electronic program guide, such as while watchingtelevision. For example, while the electronic program guide isdisplayed, recommendations may also be displayed that are based, forexample, on the programs in the electronic program guide and/or otherparameters. Also, a recommendation may look to a program the user iswatching and/or programs the user has watched, in generating arecommendation (such as by changing a user's channel to another programwhen they are finished watching a first program).

The basis by which recommendations are generated may change over time.For example, recommendations for a new show may be based initially onvarious measures of audience size and participation, or other suchinformation that is available early in a program's life. As the showbecomes more established over time, the basis of the recommendations maytransition, at least in part, to search data or click-through data froma public search engine. Such data may be available later in theprogram's life, after sufficient such data has been gathered from userinput.

For example, recommendations may be based on input received on theInternet from several users who may have watched the show, visited websites related to the show, provided user feedback related to the show,etc. Such data shows user interests, so that recommendations to a firstuser may be based on other users who have provided similar search termsfor general topics or for media-related topics, and may be generatedfrom other media-related activity by those other users. As a simplifiedexample, if a requesting user has entered searches for “Jaws,” “Orca,”and similar terms, a system may look for other users who have madesimilar queries, and may then look for commonality in othermedia-related queries submitted by those other users. For example,perhaps many of those users have also searched for “Star Trek” or“Leonard Nimoy,” or have numerous Star Trek episodes cued up in apersonalized channel (such as attached to a personal video recorder(PVR)). As a result, the first user may receive a recommendation for aStar Trek (or other sci fi) movie.

Jaws is an old movie, however, as is Star Trek. For media-programs thatare very recent, in contrast, there may be insufficient search or clickdata to make statistically satisfactory decisions about users'preferences. In such a case, popularity data (e.g., ratings data) from astructured database may be used. For example, a new television programmay be classified into a “sci fi” genre and its ratings data may showthat it is explosively popular. If a first user has been observed towatch many sci fi programs, the new program may be recommended initiallybased solely on its genre and popularity. After time, the recommendationdata may transition, either immediately or gradually, to later-developeddata such as search data. Thus, for example, perhaps the new sci fiprogram was heavily advertised and received great ratings numbers itsfirst week, but nobody liked it. Subsequent search data would indicate alack of interest, and could permit a system to lower the quality of theprogram in terms of making recommendations. In some implementations,weighting factors can be used so that the recommendation engine canapply different percentages of some approaches when multiple approachesare used in combination.

Also, where a new program is a new episode of an existing series, itsrelevance may be extrapolated to other programs or series based on therelevance of its hosting series. For example, if a new episode of theFriends sitcom is set to air soon, the series ID for Friends may be usedto identify recommendations for programs like Seinfeld or Full Housethat are judged to be similar shows, and those programs may berecommended without knowing about any program-specific relevance.Likewise, the various techniques for forming recommendations above andbelow may be mixed, such as by giving a particular weight to eachtechnique in forming a composite score for programs in makingrecommendations.

Recommendations can be made, for example, using a recommendation engineas part of a computer-based system, such as a system using centralservers to provide a number of different services, such as search, maps,shopping, and other such services. Two example categories of approachescan include “collaborative filtering” and “content-basedrecommendation.” Collaborative filtering can also be referred to as“behavioral data-based recommendation” because it uses input from manyusers to “train” the recommendation engine. The content-basedrecommendation approach generally involves analyzing the content itselfto determine similarity of items.

The collaborative filtering approach, based on data received from manyusers, trains the recommendation engine because inputs are receivediteratively over time. The collaborative filtering approach can usedifferent models for making recommendations. A user-based model (ormemory-based model) can make recommendations based on the similarity ofthe users. In one example, the similarities among users may be derivedusing information in the users' user profiles.

Another model that the collaborative filtering approach can use inmaking recommendations is the item-based model. Also known as “modelbased,” the item-based model can determine the similarities orrelationships between items by clustering them or by calculating theconditional probability of items. One such popular algorithm is BayesianNetwork and Bayesian clustering.

The source data used for collaborative filtering-based recommendationscan come from different sources, such as, for example, audiencemeasurement data from a TV provider or web-click data or other webactivity data. One advantage of using audience measurement data from aTV provider is that it can be available fairly quickly, such as withindays (e.g., a week) after a new TV show airs. Such availability can makeit possible to provide recommendations without having to wait untilenough web-click data is accumulated. Web-click data or other webactivity data can have the advantage that the data format is relativelystable, which can minimize the need to adjust to new data formats. Inaddition, web activity data may be available without having to purchaseaccess to popularity data or set up an often complex system forgathering the popularity data.

A common problem for collaborative filtering can be “lack of data” fornewly registered users and newly added items. This problem, often calledthe “cold start problem,” can be mitigated by following the “contentbased recommendation” approach. The content-based recommendationapproach can analyze the content itself to determine the similarity ofitems. Three models for mitigating such a problem are described here asexamples.

One model used for the content-based recommendation approach is to makerecommendations based on a combination of genre data (e.g., from anelectronic program guide (EPG) provider) and ratings-based popularitydata (e.g., for stations and/or programs). The model can use genre fieldvalues to find sets of similar programs. The sets may then be ranked bypopularity of the station (e.g., where the program airs) or popularityof program itself (e.g., if such data is available). Programs that areseries may be treated either at the series level or the episode level,or both. For example, when a new episode for an existing series airs, itmay be assigned popularity data reflective of the series as a whole, butthe data may transition over time into a reflection of the popularity ofthe episode, or some blend of the episode and the series (particularlywhen it is difficult to separate actions by users that indicatepopularity of a series from popularity of an episode). Also, programs,such as in the form of episodes, may be organized into common clustersother than a group of episodes in a series. Moreover, althoughrelatively implicit indications of popularity (e.g., clicking andratings) are discussed here, more explicit indications, such as “5 star”ratings systems or other user ratings may be used.

A second model used for the content-based recommendation approach canuse a filter to analyze the similarity of the programs. The filter candetermine “matching clusters” of the programs to determine set ofprograms that fall into the same category (or cluster). The filter mayuse data such as program summaries or episode synopses or otheravailable data.

A third model used for the content-based recommendation approach is toapply extra data to a filter process to produce better clustering. Forexample, a system may apply machine learning techniques to analyze thecontent of a document to determine one or more concepts of the document,in order to be able to locate a relevant document. In a media searchsetting, such analysis may be performed on various data relating to aprogram. Such extra data may include, for example, closed caption data,blogs, or some web site content that extensively describes the program.In this sense, this third model is an extension of the second model. Oneadvantage of using this model is that it can offset the lack of programdescription available in the source data from most EPG data providers.The lack of adequate descriptions can negatively affect the quality offilter-based recommendations because the filter performs clusteringusing keywords (e.g., using keywords available from programdescriptions). Using keywords derived from closed captions, blogs, orweb sites can improve recommendations when program descriptions areinadequate or unavailable.

The recommendation engine may be used, for example, when the user isperforming query-based searches for media content. Such user searchesmay, for example, be submitted when a user is using an electronicprogram guide. In addition to displaying the electronic program guide,the system may include additional displays that provide recommendationsto the user. The recommendations may be based, for example, on thepresent query from the user, on all queries from the current session ofthe user, or from a longer period of queries from the user.

Referring now to FIG. 1A, there is shown a program guide system 100 thatallows a user to search for media programming (e.g., broadcasttelevision, cable television, satellite television, broadcast radio,satellite radio, and Internet media) by making a search request using asearch page. The program guide system 100 presents, such as on thesearch results page (not shown) and/or on landing page 104, searchresults that are based on the search request. The system 100 alsopresents associated media recommendations to the user, for example,using recommendation displays 130 of FIG. 1A, 131 a and 131 b of FIG.1B, and 142 of FIG. 1C. The particular displays shown here are intendedmerely to provide an example of how a user may provide information forreceiving media-related recommendations, and may receive suchrecommendations in response. Other mechanisms for obtaining indicationsof user interest in particular programs or other topics, and forpresenting recommendations, may also be employed.

The recommendations displays 130, 131 a, 131 b and 142 each show adifferent way of generating and displaying recommendations and/orhandling a request to display recommendations. The recommendationsdisplay 130 simply ties to a particular episode of a television program(e.g., tonight's episode of The Tonight Show with Jay Leno), so it looksto information associated with that episode in finding related mediaprogramming that could serve as recommendations. For example, becauseKiefer Sutherland is a guest on the particular episode of the TonightShow, the recommendations may relate to the vampire-comedy movie LostBoys or the TV drama series 24, each of which feature Kiefer Sutherland.

In recommendations displays 131 a and 131 b of FIG. 1B, the base is acombination of multiple programs or multiple episodes. In this case, thesystem may tend not to look at episode details (though it may, so as tofind additional commonality between and among the selected shows to makefor a better recommendation). Instead, the recommendation engine maylook at general information that describes the programs (e.g., genre,starring actors, etc.), draw commonality among all such topics toidentify common topics, and then identify other programs that share suchtopics. In this sense, the recommendation engine may function similarlyto Google's Adsense (e.g., identifying topics for a web page from wordson the page, and then trying to match those topics with topics that arerelated to keywords identified by an advertiser).

To see the recommendations, the user may select a recommendations link110. Recommendations displays 131 a and 131 b relate to a group ofprograms, such as one or more series (e.g., The Tonight Show, The EarlyShow, etc.), and thus gather information about the programs in general,for example by using a structured data source such as the Internet MovieDatabase (IMDb).

Specifically, the recommendation link 131 a can be used to initiate themedia recommendations display 131 b for specific entries.Recommendations display 142 relates to a particular program, but notnecessarily about a particular episode of the program. Thus, the genreof the program may be used, as may the names of starring actors, butparticular plotlines or guest actors may not be used in determining whatother programs might bear a relation to the main program.Recommendations displays 130, 131 a, 131 b and 142 are described in moredetail below.

The system may use other “signals” in making a determination about auser's goals. For example, a user's profile may be used in combinationwith a search term in generating recommendations. Specifically, if theuser's profile includes information that the user is a movie enthusiast,a query term that includes Kiefer Sutherland may be used in combinationwith the movie enthusiast information to recommend the movie Lost Boys.

Profiles can be generated for a user and updated over time based oninput explicitly provided by the user and selections made by the user(e.g., web sites visited, user clicks on those web sites, etc.).Profiles may be, for example, maintained in social networking or othersites. If a user subsequently enters a search term that in some way isrelated to user profile information, the recommendation engine canautomatically combine the information to generate recommendations thatmay interest the user.

In one example using user profiles, collaborative filtering techniquescan be used to identify wants of other people having profiles similar tothose of the user. For instance, for someone who identifies himself asbeing between the age of 19 and 25, the system may look for TV programsthat share certain characteristics of a particular TV show (e.g., theTonight Show or whatever resulted from the user's search) within thatdemographic (e.g., ages 19-25). The system may use information based onhigh ratings from the youth demographic. The system may further useinformation from TV shows that have generally higher amounts of searchsubmissions or click-throughs from other people who identify themselvesas being young in their profiles.

In some implementations, media other than TV programs (e.g., songs, andmovies) can be recommended. For example, while the user may enter andsubmit specific query terms to search for particular types of TVprograms, the recommendation engine may further provide recommendationsfor movies, music, or the like that may interest the user. Such non-TVrecommendations may be separate from, or integrated with, TV programrecommendations.

In some implementations, the user may be able to configure theelectronic program guide—in particular the recommendation engine—torecommend certain types of media content. For example, the user may beinterested in TV and movies, but may have no interest in music CDs.Using user-controlled settings on the electronic program guide, the usermay indicate to the recommendation engine to not include music CDs whenmaking recommendations. Other user-controllable settings may also existthat allow the user to customize and/or improve the user experience ofthe electronic program guide, and in particular, the recommendationengine. In addition, the system may learn over time that certainrecommendations are not useful to a user, such as if the user seldom ornever clicks on links for such recommendations, and the system may thendemote (i.e., move down in a group of results) or remove suchrecommendations.

When a user enters a media-related search query, the system 100 maydetermine that the query is media related (e.g., by comparing it to awhite-list of media-related terms) and may present media-related searchresults. The search results may include, for example, a list of webpages having information related to the search term. In addition, thesearch results may include a list of media programming related to thesearch term. The media programming list may include text, such as “LocalTV Listings,” that identifies items in the list as media programming, asopposed to web page items. The media programming list may also includeone or more media icons that indicate the types of media programmingpresented in the list, e.g., a television, radio, or webcast icon. Thesystem can group the media results in a manner that differs from anordinary list of search results. Specifically, each of the results maybe shown with a title, time, and channel, whereas standard searchresults may be shown with a title, snippet, and URL. This specialformatting of a search result on a search results page may be referredto as a “one box.” Other search results, such as weather, location, andsimilar results may also be presented in a specially-formatted one box.

The program guide system 100 may present a landing page 104, as shown inFIG. 1A, in response to a user selection of an item in a search result.The landing page 104 includes a search box 108 where a user may input asearch term, such as a portion of a television program name, so as togenerate an updated landing page responsive to the entered search term.

The landing page 104 includes media result groupings 116. The groupings116 list one or more collections of programs related to a search term.The groupings 116 group collections of programs, for example, by programname with each item in a grouping being a particular episode or airingof the program. Alternatively, the groupings 116 may be grouped usinganother parameter, such as grouping by the media channel presenting theprograms, a genre of the programs, or the time of day the programs arepresented. An additional results control 118 allows a user to navigateto other groupings that are not currently displayed, and that may begroupings considered to be less responsive to the user's request.

Each of the groupings 116 may also include a “more” control 158 thatlists additional results within the particular grouping. In the picturedexample, the three next-pending programs are shown for the mediagrouping associated with entry 156 for the television program “TheTonight Show”, and a user can select the “more” control 158 to showaddition programs further in the future. Such a selection may cause theTonight Show grouping to expand and may also cause the other groupingsto be removed to make room for the expanded grouping.

The groupings can also include an “Add to My TV” control that, whenselected, can add a particular program (such as a series of episodes) orepisode to a personalized program guide for the user. For example, a “MyTV” channel may be maintained for a user, as described below, and anepisode or all the episodes of a program may be added to that channelwhen the “Add to My TV” control is selected.

The landing page 104 also includes a schedule grid 120. The schedulegrid 120 is displayed adjacent to and side-by-side with the groupings116. The schedule grid 120 presents programming for a particulargeographic location. A user may specify or change his or her location byselecting a change location control 122 and by making an input, such asa postal code (e.g., a ZIP code) or city and state names. The selectedlocation may also be used to determine the programs presented in thegroupings 116. Where the user is a user registered with the system 100,the user's default location may be used to generate programmingsuggestions.

The schedule grid 120 presents media programming for a particular timerange on a particular date, such as over several hours. A user mayselect the date using a calendar control 146. The calendar control 146may default to a particular date, such as the current date. When asearch is performed, the grid 120 may default to the area surroundingthe time and channel of the episode determined to be a best searchresult. Selection of other episodes in the groupings 116 may cause thegrid to move automatically to display programs around the selectedepisode (or the first-returned episode for a particular grouping, if agrouping is selected).

The schedule grid 120 presents a list of media channels vertically alongits left side and times of day horizontally along its top side in a timebar 148. The programs or episodes for a particular channel are presentedin the channel's row and in a column having a time division closest tothe actual time that the program is presented by its associated channel.The channels may be associated with a particular numerical channel for abroadcast, or may be a virtual channel such as a personalized channel ora stream of information over the internet.

The schedule grid 120 also includes a personalized channel 128, termedhere as “My TV.” The personalized channel 128 includes controls thatallow a user to create a virtual channel using content from actualchannels or another personalized channel, such as the personalizedchannel of another user. Episodes or programs may be added to thepersonalized channel 128 in a variety of ways. For example, A user mayselect a program in the schedule grid 120, and may select a command tomove it to the personalized channel 128 or may drag it to thepersonalized channel, among other things.

Also, one user may send a message to another user that identifies aparticular program, such as by supplying a URL to an online video,supplying an episode ID number, or through another accepted mechanism.In addition, the user may select a control such as the “Add to My TV”control, where that control is associated with a program or episode.

The schedule grid 120 includes the personalized channel 128. Thepersonalized channel 128 is presented near the top of the grid 120 andslightly separated from the other channels to indicate that its programsare specified by the user rather than by a media provider broadcast. Thepersonalized channel 128 can include multiple overlapping programs, anda user may be provided with various mechanisms with regard to watchingand managing such programs. As one example, the programs may bedisplayed initially according to the times they are broadcast or arefirst made available for download. The user may then drag them laterinto time so that they do not overlap, so as to “program” a viewingschedule that the user may later follow.

Programs that are shifted in time from their actual broadcast time maybe recorded when they are broadcast, such as by a PVR, and may bedisplayed according to the program the user has established. In thismanner, a user can easily select programs to view, see whether theselected programs can be viewed when they are broadcast, and view theprograms in a selected order as if they were live programs, but bytime-shifting the programs in some selected manner.

Recommendations display 130 may be displayed, for example, when the userselects or hovers over the corresponding cell in the grid 120. When therecommendation display 130 is shown, it may contain more detailedinformation than that which is displayed in the grid 120. The display130 also includes a recommendations link 110 (i.e., “If you like thisepisode . . . ”) that the user can select to view recommendations based,at least in part, on the episode displayed in the cell 130. For example,if the Tonight Show With Jay Leno is displayed in the cell 130, the usercan click on the recommendations link 110 to see one or morerecommendations related to the Tonight Show (and perhaps based on othersignals such as personalized information relating to the user). Suchrecommendations may be displayed, for example, on the details page 106of FIG. 1C.

Referring to FIG. 1B, the landing page 104 may include different typesof recommendations areas or displays. Specifically, the landing page 104can include a recommendations link 131 a that can be used to accessrecommended media programming corresponding to the media resultgroupings 116. The groupings 116 may correspond to programs displayed inthe grid 120. The recommendations display 131 a may, for example,include a title “Recommendations for Selected Programs” and bepositioned above schedule entries such as schedule entry 156. Checkboxes133 may be included adjacent to entries in the schedule entries 156. Theuser may use the checkboxes 133, for example, to control the experienceof receiving additional recommendations. For example, by checkingindividual checkboxes 133, the user may control the extent to whichrecommendations are included on the details page 106.

Recommendations area 131 b is also provided to display recommendationsgenerated in response to selection of recommendation display 131 a.Recommendations area 131 b can be split into different sections, such asa “Now” listings area 135 a and a “Later” listings area 135 b. Asdepicted, individual listings 137 a-137 e identify other late night talkshows that may be recommended, for example, because they are in the samegenre as the Tonight Show With Jay Leno and the Early Show (the twoprograms identified by the user with check mark selections). Thelistings in the recommendations area 131 b can also include networkicons 139 which visually identify the particular channel on which the TVshow airs.

Referring now to FIG. 1C, a details page 106 may be displayed when auser selects a particular program or episode so as to indicate aninterest in seeing more detailed information about that program orepisode. The details page 106 includes a program details area 132 whichitself includes an upcoming episodes area 136. The upcoming episodesarea 136 presents a list of the upcoming episodes for the program. Thelist may include detail information such as an episode title, a time forthe showing, and a channel on which the showing is to occur. The detailspage 106 also includes a search control 138. The search control 138allows a user to input a search term to initiate a search for aparticular program. The search may be limited just to a corpus ofinformation associated with programming, or may be performed on anentire web page corpus, depending on a selection from the user.

The details page 106 also includes an image details area 140. The imagedetails area 140 presents images associated with the program, such asimage result 140 a. The image result 140 a may be found by performing anInternet search for images related to the program, such as would bereturned by the standard “Google Images” service. The search may beconstrained in particular ways, such as by searching on a particularprogramming-related corpus of images or by adding certain terms, such as“television” to the query so that “Fred Thompson” returns images of theactor and not of other people. Details including a snippet, imagedetails, and a URL that displays the image, are also provided in imagedetails area 140.

The details page 106 also includes a recommendations display 142 orarea. The recommendations display 142 may list TV shows that arerecommended to the user based on the selected Tonight Show with JayLeno. The display 142 includes individual recommendation entries 142a-142 c. For example, the recommendation entry 142 a identifies the TVseries 24, which in this case may be recommended to the user because ofits association with Kiefer Sutherland—a guest on this particularepisode of The Tonight Show. Other talk shows are also shown, because oftheir similarity in genre to The Tonight Show. The recommendation entry142 a can further include a summary of the episode and one or morespecific entries for current or upcoming episodes that may interest theuser. In particular, the entries can identify the broadcasting station(e.g., Fox, CBS, etc.), specific episode dates and times, a summary ofthe episode, actors or guests, and so on. Controls may also be providedso that the user can access more recommendations and/or episodes.

In operation, a user may initiate the program guide system 100 either byinputting a search term, such as “The Tonight Show,” for a general websearch using the search control 108 or a media programming search usingthe search control 138. In the case of the search control 108, theprogram guide system 100 presents the list of programs related to thesearch term “The Tonight Show” within the search page as part of a onebox. Selecting a program in the list directs the user to the landingpage 104.

Alternatively, a user may input the search term for “The Tonight Show”using the media programming search control 138, such as is displayed onthe landing page 104 or the details page 106. The search input directsthe user to the landing page 104.

At the landing page 104, a user may direct the schedule grid 120 to aparticular channel, time, and date by selecting a program from thegroupings 116. The groupings 116 are programs determined using thesearch term “The Tonight Show.” Each program grouping includes one ormore episodes of that particular program. The user may navigate togroupings not currently presented using the additional results control118. Selecting a particular episode in a program grouping directs theschedule grid 120 to a particular channel, time, and date. The user mayalso navigate through the schedule grid 120 manually using controls,such as the calendar 146 and the time bar 148. In addition, the user may“drag” the cells in the grid 120 up, down, left, or right similar inmanner to moving a map in Google Maps.

Regarding a third grid dimension for detail level, such a dimension maybe implemented in various manners. In one such implementation, at aleast detailed level, a program title and little more may be shown in agrid so as to permit maximum density of tile display. At a more detailedlevel, a rating and a short description of an episode may be shown. At ayet more detailed level, more detailed description may be shown, and animage may be shown. At a more detailed level, information duplicating orapproaching that shown for the detail page 106 may be shown, and mayinclude recommendation information

The user may navigate to the details page 106 (see FIG. 1C) for aparticular program by selecting (e.g., clicking or double-clicking on)the program in the schedule grid 120, such as the selected program cell130. At the details page 106, a user may view detailed informationregarding the program in the program details area 132. The detailedinformation may be obtained, for example, from a structured databasethat organizes media content according to programs, actors, and othersimilar parameters and links the information in a relational manner.

The user may view images related to the program in the image detailsarea 140. The images may be obtained from a structured database, such asa database associated with the detailed information, or may be obtainedfrom disparate sources such as in the manner of Google Image Search. Theuser may navigate to an image by selecting an image result, such as theimage result 140 a.

In certain implementations, a user may select a program instance orepisode in the schedule grid 120 to generate a new list of programs inthe media result groupings 116 related to the selected schedule gridprogram. In such a situation, the selected program name or anotherprogram attribute may be submitted as a programming-directed searchrequest to the system in generating a new landing page 104. For example,if a user selects the cell for “South Park,” the grid 120 may re-centeron that cell, and the groupings 116 may include programs such as “Beavis& Butthead,” (another animated comedy), “The West Wing” (because of thedirectional South/West reference), and other similar programs.

Programs in the schedule grid 120 that also appear in the groupings 116are highlighted to indicate that they match the search criteria thatgenerated the groupings 116. Programs may also be highlighted in asimilar manner if they are a program, not part of the search result, buta recommended program for a user. The highlighting may be, for example,a shading, color, grid cell size, or cell border thickness thatdifferentiates the schedule grid programs satisfying the searchcondition from schedule grid programs that do not satisfy the searchcondition. In certain implementations, the shading, coloring, or sizingvaries based on, for example, the closeness of the match between thesearch term and the program. The shading, coloring, or sizing may alsovary with the degree of separation between programs matching the searchterm and programs related to the matching programs. One manner in whichsuch closeness or separation may be shown is by relative colors of thecells in a grid, similar to the display of a thermal map, with colorsranging steadily from blue (farthest) to red (closest), or anotherappropriate color scheme.

The schedule grid 120 has an associated calendar control 146. Thecalendar control 146 includes tabs that allow a user to select aparticular date or day of the week. Each tab includes hours of the dayassociated with the tab. Selecting a time interval in the tab directsthe schedule grid 120 to present programs for the selected day and timeinterval.

The schedule grid 120 has a time bar 148 that indicates the times of daythat programs in the schedule grid 120 are presented. The time bar 148includes controls that allow a user to move to an earlier or later timeor date. Alternatively, a user may move the schedule grid 120 by anothermethod, such as by clicking on the grid 120 and dragging the grid 120 toa new time or date. The clicking and dragging may also move the grid 120to present other channels. Alternatively, a user may use a control, suchas a scroll bar, to move through the list of channels in the grid 120.As a user moves through times, dates, and channels in the grid 120, thelanding page 104 may download data for channels and times/dates outsidethe periphery of the grid 120. This allows the grid 120 to present theprograms for the channels and times that appear as a user moves the grid120, without having to pause to download them.

The schedule grid 120 has an associated jump control 150 and anassociated filter control 152. The jump control 150 allows a user toquickly move to the current time and date in the grid 120 or to aprimetime (e.g., 8:00 PM) for the current day. The filter control 152can be used to filter out various parts of the grid. For example, thefilter may be used to show only prime time or late night programming, sothat, for example, the grid jumps from 11:00 PM directly to 8:00 PM thenext day. Likewise, the filter can be used to show only channels in aparticular category, such as only movies channels or sports channels, orchannels specifically selected by a user as their “favorites” channels.

FIG. 2 shows a graph 200 of a general correlation between two forms ofaudience preference data over time. Specifically, the graph 200 depictsthe relationship in data quality related to available audiencepreference data over time. The graph's vertical axis is data quality,and its horizontal axis is time. The time is measured in weeklyincrements (e.g., Weeks 1-11) which can correspond, to the availabilityof new audience data available after weekly showings of a TV program.The graph 200 includes a user click data line 202 and an audiencemeasurement data line 204. The height of the lines 202 and 204 representrelative data quality over time. For example, the line 202 representsthe quality of user click data 202.

Initially, both lines 202 and 204 represent zero quality, such as atWeek 0 when a TV show first airs. At this early stage in a TV program'sexistence, the height of line 202 demonstrates that a common problem indata quality for collaborative filtering can be “lack of data” for newlyregistered users and newly added items. In particular, the graph 200shows that user click data may not be present initially for amedia-related program. For example, when a program first premiers, noone knows about it, and few users have made searches for it. Also, thereare very few websites related to it, so that there is very little toclick to. In contrast, audience measurement data can be determinedimmediately, and provides some indication of the popularity of aprogram. Such data may also be broken out into various demographictopics that can be used for correlating interests of a first user withinterests of other users so that recommendations may be made morepersonalized.

At Week 1, for example, user click data 202 may achieve a relativelysmall, but not zero, data quality. In contrast, the audience measurementdata 204 may increase significantly, for example, as the first week ofaudience ratings are available for the show. In this way, the quality ofaudience measurement data 204 exceeds that of user click data 202, atleast early in the TV show's existence.

At Weeks 2-7, audience measurement data 204 can continue to grow, forexample, at a slower rate than that of the Week 1 spike. Over time,audience measurement data 204 may tend to level off, for example, asNielson ratings provide less and less additional information related tothe show. During the same time, however, the data quality of user clickdata 202 can continue to grow significantly as increasingly more userswatch the show and provide their input. As depicted at Week 7, the dataquality of user click data 202 may eventually surpass that of theaudience measurement data 204, which has essentially leveled off. Inthis way, the potential long-term superiority of user click data 202 canexceed the data quality of audience measurement data 204. In particular,the click data may be more accurate and may also be available toorganizations without having to make payments to traditional datagathering organization or without having to set up traditional datagathering systems.

As can be seen in FIG. 2, the audience measurement data 204 showsaverage audience popularity, or data quality, for each episode and topsout over time. In addition, the user click data 202 also tops out, buthere shows additional popularity, or data quality, for a particularprogram as compared to audience measurement data 204. Different reasonscan exist for the data quality of user click data 202 to surpass that ofaudience measurement data 204. Specifically, perhaps the program hasinterest that cannot be captured by traditional audience measurementdata 204. For example, user click data 202 may include datacorresponding to a rabid interest among certain fans in a particulardemographic. In another example, user click data 202 may be based onuser viewing via mediums such as online media stores and DVDs.

In order to benefit from the data quality that varies in time for userclick data 202 and audience measurement data 204, the recommendationengine may transition in how it uses the data over time. For example,the recommendation engine may initially rely on audience measurementdata 204 for a few weeks after a new TV program airs. The recommendationengine may transition into using user click data 202 once suchadditional data is available in large enough numbers to be reliable.

FIG. 3 is a schematic diagram of a computer-implemented system 300 forproviding media-related recommendations over a network. The system 300includes a TV Client 302 (e.g., implemented in JavaScript, HTML, Flash,etc.) that is communicatively coupled to a TV front end 304. The TVClient 302 can serve as the user interface for the user's interactionwith the TV, such as for searching for TV shows and receivingrecommendations. TV front end 304 can serve as the behind-the-scenesinterface for searching media programming, storing user clickinformation, processing recommendations, etc.

A user-click history 306 can store user click information received fromthe TV front end 304. For example, the user click information can tracka user's preference for TV shows, such as the genre of TV shows, thenames of particular programs, the names of particular personalities,etc.

A recommendation generator 308 can use data from the user-click history306 to generate recommendations based on various attributes. In thissense, the recommendation generator 308 can serve as the “recommendationengine” described above. The recommendation generator 308 can also useaudience measurement data 310 in generating recommendations. Forexample, the audience measurement data 310 may represent viewerpopularity collected over time, such as by Nielson ratings. Moreover,the recommendation generator may also draw upon corpus data 309, whichmay include information such as blogs, media-directed web sites, andother such media-directed web content. Such information may likewise beused to discern relationships between and among particular programs forpurposes of determining whether two programs are sufficiently relatedthat a recommendation can be made for one based on a determination thata user is interested in the other.

A recommendation repository 312 can store recommendations produced bythe recommendation generator 308. Such recommendations may be stored fora short or long periods of time, and may be purged when determined to beobsolete. Recommendations from the recommendation repository 312 can beused by the TV front end 304 when recommendations are to be displayed tothe user.

A genre-based index 314 can contain information about TV shows organizedby genre. Genre ratings may reflect, for example, the popularity ofparticular programs and/or stations. The genre-based index 314 can beused by the TV front end 304 when genre-based information is needed.

Tables 316 can include information used by the electronic program guide,such as information that is used to build individual cells in a programguide grid. Information from the tables 316 may be provided to the TVfront end 304 when, for example, the user first displays a programmingguide grid.

A data loader 318 can load information into the genre-based index 314and tables 316. The information that the data loader 318 uses may beprovided by TV programming providers (e.g., networks, cable stations,satellite companies, etc.) In particular, the data loader 318 mayreceive and load channel line-up and schedule information (e.g., from acommercial provider), such as information indicating that, at 5 PM,Robot Chicken is on Comedy Central on Channel 15. It may also concertsuch data into a form, such as particularly formatted tables, that canbe loaded by the front end 304. The recommendation generator 308 mayalso relies on the tables 316 in order to generate a recommendationrepository.

A search engine 320 can service program media search requests receivedby the TV front end 304. For example, a search query entered by a useron the TV client 302 can be received by the TV front end 304 andprovided to the search engine 320. The search engine 320 can provide theresults of the search to the TV front end 304 for use by the TV client302.

In one example process flow of the system 300, processing can begin atarrows 1 and 2 when the data loader 318 generates genre-based index 314and tables 316. The data loader 318 may use various sources ofinformation in generating the tables 316, such as information providedby TV programming providers. When updating the genre-based index 314,additional indexes may be added that allow the information to be sortedby genre code of the program and sub-sorted by the popularity scorebased on given ratings-based popularity of stations and program. Inputsto the data loader 318 may be in the form of text files, one or more foreach station, and may optionally include program popularity.

In the next step (e.g., at arrow 3), the TV front end 304 can useinformation from the tables 316 to load tables for the programming guidegrid, etc. The process of loading the grid can occur, for example, whenthe user first clicks on the TV, or as updated information is available.

At the same time (e.g., at arrow 4), the TV front end 304 can useinformation from the genre-based index 314 to load genre-related tablescorresponding to programs that the user can view, search or receiverecommendations. The process of loading the genre-related tables canoccur, for example, when the user first clicks on the TV, or as updatedinformation is available.

Once the TV front end 304 has sufficient data to service initial userrequests (e.g., steps corresponding to arrows 1-4 are complete), theuser can begin to make requests using the information. For example, theuser may be watching TV and select a control to display the programguide grid. The TV client 302 can report the selection back to TV frontend 304. To respond to the request, the TV front end 304 can provide thegrid data to the TV client 302 for display, for example, on the TVscreen.

Providing the program guide grid may correspond, for example, to asearch request made by the user for particular programming. For example,the program guide grid may contain TV show listings corresponding tosearch terms that the user enters on the TV client 302. The TV front end304 can receive the query via arrow 7, and provide the search query tothe search engine 320 via arrow 6. The search engine 320 can process thequery and provide the search results to the TV front end 304, which canprovide the search results to the TV client 302 for display, forexample, on the user's TV, in a manner like that discussed above.

Over time, user activity on the TV client 302 collected by the TV frontend 304 (via arrow 7) may be provided to the user click history 306 viaarrow 8. Such user activity may include user actions that can be usedlater, for example, in formulating recommendations. For example, theuser actions may include inputs by the user on the Internet that relateto particular TV programs or stations. In this way, the recommendationscan be based, at least in part, on the collaborative filtering approachdescribed above or other similar approaches. Moreover, when more thanone user is involved, recommendations using the collaborative filteringapproach are “behavioral data-based recommendations” because they canuse input from many users to “train” the recommendation engine.

To generate recommendations, the recommendation generator 308 can usedata from the user-click history 306 (e.g., corresponding to user inputsreceived over time) via arrow 9, and audience measurement data 310 viaarrow 11. The recommendations that are generated can be stored in therecommendation repository 312 via arrow 10. Such newly-generatedrecommendations can be used (by arrow 5) by the TV front end 304 as theyare updated in the recommendation repository 312.

FIG. 4A is a flow chart of a process 400 for providing media-relatedrecommendations in response to a search query. In general, the process400 shows that recommendations are based on the topics corresponding tothe search results. For example, the recommendations may be made formedia search results for a user at a client who is browsing mediaprogramming information, such as in the screen shots shown in FIGS. 1Athrough 1C.

At box 402, the system receives a search request, which may have beensubmitted through a search request box for a standard search engine. Forexample, the search request may correspond to a search query (e.g., “thetonight show”) entered by a user in search box 108. The systemidentifies the topic of the search result at box 404. The topic maycorrespond, for example, to media programming that matches keywords inthe search result (e.g., based on the search query). For example, thetopic of the search result based on the search query “the tonight show”may include talk shows such as The Tonight Show With Jay Leno.

At box 406, the system identifies topics correlating to the searchresult. The correlation may include, for example, the genre ofprogramming that corresponds to programs matching those topics of thesearch result. For example, genre-based correlations may correlate othertalk shows even if the titles of the shows don't match terms in thesearch query. The correlation can also include other media programmingthat may be related to the shows in other ways. For example, if thesearch result includes a Tonight Show With Jay Leno episode featuringKiefer Sutherland, the correlation may correlate other media programmingrelated to Kiefer Sutherland, such as other TV shows, movies, etc. Thecorrelation can also use information from other sources, such as userprofiles. For example, if the user's user profile identifies apreference for Kiefer Sutherland, that information can be used in theidentification of topics correlating to the search result.

At box 408, the system offers the recommendations to the user. Forexample, the recommendations may be sent to the user's TV and madeavailable with the search results that matched the user's search query.In some implementations, the recommendations may be offered, but notnecessarily displayed until the user opts to display therecommendations. For example, referring to FIG. 1A, the recommendationsmay not be displayed until the user selects the recommendation link 110.

At box 410, the system displays the recommendations. For example,referring to FIG. 1B, the recommendations may be displayed on thelanding page 104 as recommendations 131 b. In another example, referringto FIG. 1C, the recommendations may be displayed as recommendations 142.Regardless of how the recommendations are displayed, they are one stepremoved from the search results. Specifically, the recommendationscorrespond to the topics associated with the search results, but are nota subset of the search results themselves.

FIG. 4B is a flow chart of a process 416 for providing media-relatedrecommendations. The process 416 shows how the recommendation engine maydetermine when to use user click data, traditional audience measurementdata (or other such popularity determining data), or both. Specifically,the process 416 shows the switchover over time from traditional audiencemeasurement data to user click data. The switchover may occur, forexample, as the data quality of user click data is considered to be ahigher quality than that of audience measurement data. In certainimplementations, audience measurement data may always be used to someextent, and be blended with other data for making recommendations.Various weightings, from zero to 100%, may be given to the variousinputs described here and other inputs, in handling various inputs for arecommendation system and process.

The recommendation engine can use such data quality considerations inmaking recommendations. For example, when insufficient user click datais available, the process can use traditional audience measurement datain generating recommendations. By contrast, when excellent user clickdata exists, the recommendation engine may base its recommendationsmore-so, or in some situations, even exclusively, on user click data.Finally, when fair (e.g., somewhere between insufficient and excellent)user click data is available, the recommendation engine may use acombination of both types of data. In this case, the two types of datamay be blended in some way, such as in the union of recommendation setsfrom both sources.

At step 418, the system receives a search query and transmits theresults. For example, referring to FIG. 1A, the user may enter a searchquery such as “the tonight show” in the search box 108. As a result, thesystem may provide search results matching the query, such as mediaresult groupings 116.

At step 420, the system receives a recommendation request. For example,the recommendation request may be made automatically on the user'sbrowser, or the user may initiate the request by selecting a controlsuch as the recommendations link 110.

At this point in the process 416, the recommendation engine can assessthe quality of available user click data and traditional audiencemeasurement data. As described above in regards to the collaborativefiltering approach, the availability and quality of data over time canchange. For example, there may be a “lack of data” for newly registeredusers and new TV programs. Specifically, new TV programs can exhibit a“cold start problem.” Initially, the quality of user-click data may bepoor, but may improve over time. By contrast, the quality of audiencemeasurement data may be good after the first several episodes of a TVprogram, but its quality may level off over time. Ifinsufficient-quality user click data is available at box 422, the leftbranch of the flow diagram of FIG. 4B can be used. If fair-quality userclick data is available at box 424, the center branch of the flowdiagram can be used. If excellent-quality user click data is availableat box 426, the right branch of the flow diagram can be used.

When insufficient-quality user click data is available at box 422, thesystem accesses audience data at box 428. For example, the audience dataaccessed may be from various audience ratings measures that are updatedand available, for example, when a TV show airs each week. The systemgenerates correlations at box 430. Correlations can be made, forexample, between the audience measurement data and the topics associatedwith the search results. For example, the correlations may associatecertain genres of TV shows that are popular with audiences with the TVshows identified in the search results.

When excellent-quality user click data is available at box 426, thesystem accesses user click data at box 436. For example, the user clickdata may include input received from users over time, such as onwebsites that may collect user reactions to TV shows. The systemgenerates correlations at box 438. Correlations can be made, forexample, between the user click data and the topics associated with thesearch results. For example, the correlations may associate certain TVstations or TV show episodes that are popular with users with the TVshows identified in the search results.

When fair-quality user click data is available at box 424, the systemaccesses a combination of user click data and audience measurement dataat box 432. The system generates and blends correlations at box 434. Thetwo types of data may be blended so that the data quality of each datatype can be exploited. For example, while the audience measurement datamay indicate a show is very popular among TV viewers, user click datamay provide additional insight that is not tracked or available, forexample, by audience participation measures. In particular, the userclick data may identify, for example, a theme or observation that the TVproducers have not considered (that audience participation data does notconsider). In another example, while the audience measurement data mayindicate an initial strong popularity of TV audiences, user click datamay indicate that users are just starting to tire of the TV show.Subsequent correlations are then made between both data sources and thesearch results.

Blending can be done in different ways. For example, blending may use amathematical model that considers the quality of data sources over time.Early in a show's life (e.g., after a few weeks), the mathematical modelmay use a higher percentage (e.g., 70-90%) of information from audiencemeasurement data, and a lesser percentage (e.g., 10-30%) of informationfrom user click data. When both types of data are expected to havesimilar qualities, the percentages may be 50-50. Late in a show's life,such as after the show is in reruns, blending may use a much higherpercentage (e.g., 90%) of user-click data.

User click data can also be blended with personalized information suchas a user's age, hobbies, or other interests. This type of informationmay be available, for example, from user profiles. In someimplementations, users may be notified that their personalizedinformation is being used in generating recommendations, and the usermay be able to optionally block such use of their personalizedinformation. In this manner, the system can help dispel some concernabout privacy issues.

The blending and correlating can change over time due to the nature ofthe different approaches (e.g. “collaborative filtering” and“content-based recommendation.”) As described above, the collaborativefiltering approach uses input received from many users to “train” therecommendation engine. When sufficient user input is available, therecommendation engine can rely such user input in making correlations.The contribution of collaborative filtering can improve over time, forexample, as more users provide input on specific TV shows. At the sametime, the recommendation engine can typically always use thecontent-based recommendation approach, analyzing the content itself tofind out the similarity of items.

The content-based recommendation approach, as described above, cananalyze the content itself to determine the similarity of items, and mayuse models: genre-based, filter-based, and filter-based using extradata. When the genre-based model is used, the correlations can be made,for example, between TV shows of the same type (e.g., late night talkshows). The correlations may be based on a combination of genre data(e.g., from EPG provider) and ratings based popularity data (e.g., forstations and/or programs). The model can use genre field values to findsets of similar programs. The sets may then be ranked by popularity ofthe station (e.g., where program airs) or popularity of series itself(e.g., if data is available).

When the filter-based model is used, a filter can be used to analyze thesimilarity of the programs. The filter can determine “matching clusters”of the programs to determine set of programs that fall into the samecategory (or cluster).

When the model used applies extra data to the filter process, betterclustering can result. Such extra data may include, for example, closedcaption data, blogs, or some web site contents that extensively describethe program. In this sense, this third model is an extension of thesecond model.

At box 440, the system assigns scores to the recommendations that havebeen generated, such as by generating and blending correlations in boxes430, 434 and 438. Scores may be based on how well a recommendationcorrelates to the topic of the search result. For example, arecommendation that correlates in multiple ways to the search resultsmay receive a higher score than one that correlates in fewer ways. Forinstance, if the query results are associated with Kiefer Sutherland,recommendations that correlate to Kiefer Sutherland can be scoredhigher. Moreover, if information in the user's personal informationindicates a liking for Kiefer Sutherland, the correspondingrecommendations may be scored higher yet.

At box 442, the system transmits ordered recommendations. Therecommendations may be sorted based on the scores assigned in box 440.In this way, when the user receives the recommendations, the highestscoring recommendations may be display first or at the top of the list.

FIG. 4B is a flow chart of a process 450 for providing media-relatedrecommendations for new programs. In general, the process 450 shows oneexample for handling requests relating to new or relatively newprograms, and generating recommendations relating to such programs, Atbox 452, an identification of a new program is received. For example, auser may have selected the program from an upcoming area of anelectronic program guide grid, or may have entered the title of theprogram after seeing promotional ads for the upcoming program.

At box 454, the process 450 determines that the program is new. Forexample, the process 450 may attempt to check such entries againstmedia-related click and/or audience measurement data, and may not finddata in sufficient numbers. The system may infer from such lack of datathat the program has not appeared before, at least not in wide release.Thus, at box 456, the process 450 may identify a series associated withthe program. Using the series identification, the process at box 458 mayidentify other series have sufficiently relating factors, such as commongenres, plot descriptions, actors, etc., that those other series may bedeemed to be something in which a user would be interested, given theuser's interest in the first episode. In such a situation, therecommended programs may be ranked according to a degree of commonalitywith the initial episode, by popularity measures of the other programs,or both.

With a particular recommended series identified, the process 450 thensearches for episodes of the program that will be airing in the nearfuture, such as in the next several hours or next several days or weeks.Information about these episodes may then be returned to the user (box462), and the user may be given the opportunity to review them, such asby adding one or more episodes to a personalized channel for the user.This process 450 is thus one example of an approach to be used inproviding recommendations for relatively new programs. While the episodeis reviewed based on a its series cluster, the episode could also beassigned to a variety of other clusters, and those cluster may be usedfor making recommendations like those discussed here.

FIG. 5 is a swim lane diagram showing interaction of various componentsin a media-related recommendation process. As depicted, the componentsinclude a client, a recommendation server, a click server and a mediatracking server. The client can correspond, for example, to the user'sTV set. The recommendation server can be, for example, computer softwarethat generates recommendations corresponding to search results matchinga user's search query for media programming. In general, therecommendation server may actually provide recommendations for all sortsof things, including any media related recommendations. The clickserver, for example, can logically include the set of various onlinewebsites, etc., that receive input from users, such as users who providetheir feedback and opinions regarding TV shows. In one example, theclick server can be a general program at a company like Google thattracks clicks for various sorts of applications. The media trackingserver, for example, may be a media tracking server run by a companylike Nielsen.

At box 502, the system loads indices, such as indices corresponding toTV programs, etc. Index loading can be performed by the recommendationserver, which may set up the indices in preparation for receivingaudience data corresponding to the TV shows.

At box 504, the recommendation server obtains audience data. Forexample, the recommendation server may request the audience data for aparticular show from the media tracking server. In some implementations,the recommendation server may be configured to automatically receiveaudience data from the media tracking server, such as on a scheduledbasis (e.g., when the audience ratings are updated after each week'sshowing of a particular show).

The media tracking server provides the audience data at box 506. Theaudience data provided can be in response to a specific request by therecommendation server, or it may be, for example, a scheduledtransmission of audience data.

The recommendation server obtains user click data at box 508. Obtaininguser click data for a particular program can begin almost immediately,for example, such as when users begin commenting on TV programs thathave just aired (or are soon to air) on TV.

The click server provides the click data to the recommendation server atbox 510. For example, the click data provided can be in response toclick data requested by the recommendation server, such as for aparticular TV show. Obtaining updated click data can occur over time,effectively incorporating better or additional click data that mayrepresent newer input from users.

The client receives the search request at box 514. For example,referring to FIG. 1A, the search request may be based on a search querythat the user enters in the search box 108 to find media programmingrelated to query “the tonight show”.

The client displays the results at box 516. The results may be a list ofsearch results, such as a list of TV shows related to the search query.For example, in response to the search query “the tonight show,” thesystem may display media result groupings 116 that include other talkshows, etc.

The client receives the selection at box 518. The selection refers tothe selection made by the user, for example, from the media resultgroupings 116. For example, the user may select (e.g., by clicking on)one of the shows in the media result groupings 116, or the user mayperform some other action on the page. The client can notify the clickserver of the one or more user clicks.

The click server records the selection at box 520. Based on the userselection received by the client, the click server can add the useraction to its collection of user click data. In this way, the clickserver can track user clicks of multiple users over time. The user clickdata that the click server records may be indexed in various ways, suchas by user, title (e.g., TV show name and/or episode), media type (e.g.,TV, etc.), genre (talk show, game show, drama, etc.), TV show (or moviename), personality (e.g., actor, entertainer, guests, etc.), or otherattributes. Such indices can form the basis for the recommendationengine to use the “collaborative filtering” approach as described above.

The client requests recommendations at box 522. For example, referringto FIG. 1B, the user may select the recommendations link 131 a in orderto request the display of media recommendations 131 b. In anotherexample, referring to FIG. 1A, the user may select the recommendationslink 110 in order to request the display of details page 106 containingthe media recommendations 142. The recommendation request can be sent tothe recommendation server and may identify the search results matchingthe user's original search query. The request may also identify whetherthe user's personalized information is to be used to form therecommendations

At box 524, the recommendation server identifies the recommendationcontext. The context identified depends on the recommendation requestreceived from the client. For example, the recommendation server mayidentify the context using the search results corresponding to thesearch query. The context identified may include the genre of mediaprogramming or the names of personalities associated with the mediaprogramming. The context may also incorporate the whether the user'spersonalized information is to be used to form the recommendations.

The recommendation server finds correlations at box 526. For example,the correlations may be formed among media programming forming the basisof the recommendations. The correlations may, in some implementations,use the topical correlations generated at box 512. In someimplementations, the correlations can include the use of clustering,such as correlations based on common keywords, etc.

At box 528, the recommendation server stores the correlations. Storingthe correlations can eliminate the need to recalculate similarcorrelations later, such as when additional media recommendations aregenerated for similar circumstances (e.g., similar search results,similar genre, etc.).

The recommendation server provides recommendations at box 530. Forexample, the recommendation server can provide the recommendations tothe client for display. The recommendations provided may be those thathave just been generated, or those that have been accessed from storage,of some combination of both.

The client displays recommendations at box 532. For example, referringto FIGS. 1B and 1C, the recommendations received from the recommendationserver may be displayed as media recommendations 131 b or mediarecommendations 142.

The client receives the selection at box 534. For example, in responseto the recommendations displayed, the user may select one or more ofthem to review, etc. In particular, the user selection may trigger theclient to display additional information associated with the recommendedmedia program. Such user selection can also represent user click dataindicating, for example, the popularity of a particular TV program.

The click server records the selection box 536. For example, based onthe selection that the user made from the displayed recommendations onthe client, the click server can update the user click data that theclick server maintains. Updating the user click data over time can allowthe click server to provide click data (at box 510) as needed to therecommendation server.

FIG. 6 shows an example of a computer device 600 and a mobile computerdevice 650 that can be used to implement the techniques described here.Computing device 600 is intended to represent various forms of digitalcomputers, such as laptops, desktops, workstations, personal digitalassistants, servers, blade servers, mainframes, and other appropriatecomputers. Computing device 650 is intended to represent various formsof mobile devices, such as personal digital assistants, cellulartelephones, smart phones, and other similar computing devices. Thecomponents shown here, their connections and relationships, and theirfunctions, are meant to be exemplary only, and are not meant to limitimplementations of the inventions described and/or claimed in thisdocument.

Computing device 600 includes a processor 602, memory 604, a storagedevice 606, a high-speed interface 608 connecting to memory 604 andhigh-speed expansion ports 610, and a low speed interface 612 connectingto low speed bus 614 and storage device 606. Each of the components 602,604, 606, 608, 610, and 612, are interconnected using various busses,and may be mounted on a common motherboard or in other manners asappropriate. The processor 602 can process instructions for executionwithin the computing device 600, including instructions stored in thememory 604 or on the storage device 606 to display graphical informationfor a GUI on an external input/output device, such as display 616coupled to high speed interface 608. In other implementations, multipleprocessors and/or multiple buses may be used, as appropriate, along withmultiple memories and types of memory. Also, multiple computing devices600 may be connected, with each device providing portions of thenecessary operations (e.g., as a server bank, a group of blade servers,or a multi-processor system).

The memory 604 stores information within the computing device 600. Inone implementation, the memory 604 is a volatile memory unit or units.In another implementation, the memory 604 is a non-volatile memory unitor units. The memory 604 may also be another form of computer-readablemedium, such as a magnetic or optical disk.

The storage device 606 is capable of providing mass storage for thecomputing device 600. In one implementation, the storage device 606 maybe or contain a computer-readable medium, such as a floppy disk device,a hard disk device, an optical disk device, or a tape device, a flashmemory or other similar solid state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. A computer program product can be tangibly embodied inan information carrier. The computer program product may also containinstructions that, when executed, perform one or more methods, such asthose described above. The information carrier is a computer- ormachine-readable medium, such as the memory 604, the storage device 606,memory on processor 602, or a propagated signal.

The high speed controller 608 manages bandwidth-intensive operations forthe computing device 600, while the low speed controller 612 manageslower bandwidth-intensive operations. Such allocation of functions isexemplary only. In one implementation, the high-speed controller 608 iscoupled to memory 604, display 616 (e.g., through a graphics processoror accelerator), and to high-speed expansion ports 610, which may acceptvarious expansion cards (not shown). In the implementation, low-speedcontroller 612 is coupled to storage device 606 and low-speed expansionport 614. The low-speed expansion port, which may include variouscommunication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet)may be coupled to one or more input/output devices, such as a keyboard,a pointing device, a scanner, or a networking device such as a switch orrouter, e.g., through a network adapter.

The computing device 600 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as astandard server 620, or multiple times in a group of such servers. Itmay also be implemented as part of a rack server system 624. Inaddition, it may be implemented in a personal computer such as a laptopcomputer 622. Alternatively, components from computing device 600 may becombined with other components in a mobile device (not shown), such asdevice 650. Each of such devices may contain one or more of computingdevice 600, 650, and an entire system may be made up of multiplecomputing devices 600, 650 communicating with each other.

Computing device 650 includes a processor 652, memory 664, aninput/output device such as a display 654, a communication interface666, and a transceiver 668, among other components. The device 650 mayalso be provided with a storage device, such as a microdrive or otherdevice, to provide additional storage. Each of the components 650, 652,664, 654, 666, and 668, are interconnected using various buses, andseveral of the components may be mounted on a common motherboard or inother manners as appropriate.

The processor 652 can execute instructions within the computing device650, including instructions stored in the memory 664. The processor maybe implemented as a chipset of chips that include separate and multipleanalog and digital processors. The processor may provide, for example,for coordination of the other components of the device 650, such ascontrol of user interfaces, applications run by device 650, and wirelesscommunication by device 650.

Processor 652 may communicate with a user through control interface 658and display interface 656 coupled to a display 654. The display 654 maybe, for example, a TFT LCD (Thin-Film-Transistor Liquid Crystal Display)display or an OLED (Organic Light Emitting Diode) display, or otherappropriate display technology. The display interface 656 may compriseappropriate circuitry for driving the display 654 to present graphicaland other information to a user. The control interface 658 may receivecommands from a user and convert them for submission to the processor652. In addition, an external interface 662 may be provide incommunication with processor 652, so as to enable near areacommunication of device 650 with other devices. External interface 662may provide, for example, for wired communication in someimplementations, or for wireless communication in other implementations,and multiple interfaces may also be used.

The memory 664 stores information within the computing device 650. Thememory 664 can be implemented as one or more of a computer-readablemedium or media, a volatile memory unit or units, or a non-volatilememory unit or units. Expansion memory 674 may also be provided andconnected to device 650 through expansion interface 672, which mayinclude, for example, a SIMM (Single In Line Memory Module) cardinterface. Such expansion memory 674 may provide extra storage space fordevice 650, or may also store applications or other information fordevice 650. Specifically, expansion memory 674 may include instructionsto carry out or supplement the processes described above, and mayinclude secure information also. Thus, for example, expansion memory 674may be provide as a security module for device 650, and may beprogrammed with instructions that permit secure use of device 650. Inaddition, secure applications may be provided via the SIMM cards, alongwith additional information, such as placing identifying information onthe SIMM card in a non-hackable manner.

The memory may include, for example, flash memory and/or NVRAM memory,as discussed below. In one implementation, a computer program product istangibly embodied in an information carrier. The computer programproduct contains instructions that, when executed, perform one or moremethods, such as those described above. The information carrier is acomputer- or machine-readable medium, such as the memory 664, expansionmemory 674, memory on processor 652, or a propagated signal that may bereceived, for example, over transceiver 668 or external interface 662.

Device 650 may communicate wirelessly through communication interface666, which may include digital signal processing circuitry wherenecessary. Communication interface 666 may provide for communicationsunder various modes or protocols, such as GSM voice calls, SMS, EMS, orMMS messaging, CDMA, TDMA, PDC, WCDMA, CDMA2000, or GPRS, among others.Such communication may occur, for example, through radio-frequencytransceiver 668. In addition, short-range communication may occur, suchas using a Bluetooth, WiFi, or other such transceiver (not shown). Inaddition, GPS (Global Positioning System) receiver module 670 mayprovide additional navigation- and location-related wireless data todevice 650, which may be used as appropriate by applications running ondevice 650.

Device 650 may also communicate audibly using audio codec 660, which mayreceive spoken information from a user and convert it to usable digitalinformation. Audio codec 660 may likewise generate audible sound for auser, such as through a speaker, e.g., in a handset of device 650. Suchsound may include sound from voice telephone calls, may include recordedsound (e.g., voice messages, music files, etc.) and may also includesound generated by applications operating on device 650.

The computing device 650 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as acellular telephone 680. It may also be implemented as part of asmartphone 682, personal digital assistant, or other similar mobiledevice.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms “machine-readable medium”“computer-readable medium” refers to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs)) used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term “machine-readable signal” refers to any signal used to providemachine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing system that includes a back end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here), or any combination of such back end, middleware, orfront end components. The components of the system can be interconnectedby any form or medium of digital data communication (e.g., acommunication network). Examples of communication networks include alocal area network (“LAN”), a wide area network (“WAN”), and theInternet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

A number of embodiments have been described. Nevertheless, it will beunderstood that various modifications may be made. For example, variousforms of the flows shown above may be used, with steps re-ordered,added, or removed. Also, although several applications of the electronicprogram guide systems and methods have been described, it should berecognized that numerous other applications are contemplated. Moreover,although many of the embodiments have been described in relation toelectronic program guides, that term should be understood to includevarious forms of mechanisms for displaying media-related content andscheduling information for such content. Accordingly, other embodimentsare within the scope of the following claims.

1. A computer-implemented method, comprising: receiving, at a computersystem, information expressing a user's interest in one or more mediaprograms; obtaining information indicative of popularity for a pluralityof media programs responsive to the received information and identifyinga host series for at least one of the plurality of media programs;identifying one or more other media programs based at least in part onthe host series; and generating and transmitting a recommendation of theidentified one or more other media programs for display to the userbased at least in part on the relevance of the identified host series tothe one or more other media programs and the information indicative ofpopularity.
 2. The method of claim 1, wherein the received informationcomprises an explicit query.
 3. The method of claim 1, wherein the hostseries comprises a plurality of related media program episodes.
 4. Themethod of claim 1, wherein the identified host series is not the hostseries of the media program in which the user expressed interest.
 5. Themethod of claim 1, wherein the received information includes informationindicative of media programs viewed by the user.
 6. The method of claim1, wherein one or more recommendations are generated based at least inpart on a series identifier for the host series.
 7. The method of claim1, further comprising generating data for displaying an electronicprogram guide grid that includes the one or more recommendations.
 8. Anon-transitory computer-readable medium storing software comprisinginstructions executable by one or more computers which, upon suchexecution, cause the one or more computers to perform operationscomprising: receiving, at a computer system, information expressing auser's interest in one or more media programs; obtaining informationindicative of popularity for a plurality of media programs responsive tothe received information and identifying a host series for at least oneof the plurality of media programs; identifying one or more other mediaprograms based at least in part on the host series; and generating andtransmitting a recommendation of the identified one or more other mediaprograms for display to the user based at least in part on the relevanceof the identified host series to the one or more other media programsand the information indicative of popularity.
 9. The non-transitorycomputer-readable medium of claim 8, wherein the received informationcomprises an explicit query.
 10. The non-transitory computer-readablemedium of claim 8, wherein the host series comprises a plurality ofrelated media program episodes.
 11. The non-transitory computer-readablemedium of claim 8, wherein the identified host series is not the hostseries of the media program in which the user expressed interest. 12.The non-transitory computer-readable medium of claim 8, wherein thereceived information includes information indicative of media programsviewed by the user.
 13. The non-transitory computer-readable medium ofclaim 8, wherein one or more recommendations are generated based atleast in part on a series identifier for the host series.
 14. Thenon-transitory computer-readable medium of claim 8, further comprisinggenerating data for displaying an electronic program guide grid thatincludes the one or more recommendations.
 15. A computer-implementedsystem, comprising: memory storing data relating to popularity of one ormore media programs; an interface configured to receive media-relatedrequests and information indicative of popularity, to generate responsesto the requests, and to generate recommendations different from theresponses, wherein the recommendations include information about the oneor more media programs and are based at least in part on a relevance ofa host series to the one or more other media programs and based at leastin part on the information indicative of popularity; and a programmingguide builder to generate code for constructing a programming guidecontaining programs responsive to the media related requests anddisplaying popularity indications for the programs in the guide.
 16. Thecomputer-implemented system of claim 15, wherein the recommendations aregenerated from audience participation data associated with the mediaprograms.
 17. The computer-implemented system of claim 15, wherein theprogramming guide comprises a listing of upcoming episodes of programsidentified by the recommendation.
 18. The computer-implemented system ofclaim 15, further comprising a collaborative filter for determiningco-dependency relationships between users and media programs.